The present disclosure relates to an evaluation predicting device, an evaluation predicting method, and a program.
Recently, an enormous amount of information has come to be provided to users through broader-band networks. It has therefore become difficult on the part of a user to search the enormous amount of information provided for information sought by the user. On the other hand, from the viewpoint of an information providing side, the information desired to be provided to a user is buried in the enormous amount of information, and such information is difficult for the user to peruse. In order to remedy such a situation, progress has been made in devising mechanisms for accurately extracting information preferred by a user from the enormous amount of information and providing the information to the user.
Filtering methods referred to as collaborative filtering and content-based filtering, for example, are known as mechanisms for extracting information preferred by a user from an enormous amount of information. In addition, there are kinds of collaborative filtering including user-based collaborative filtering, item-based collaborative filtering, matrix factorization-based collaborative filtering (see Ruslan Salakhutdinov and Andriy Mnih, “Probabilistic Matrix Factorization,” in Advances in Neural Information Processing Systems, volume 20, 2008, hereinafter referred to as Non-Patent Document 1), and the like. On the other hand, there are kinds of content-based filtering including user-based content-based filtering, item-based content-based filtering, and the like.
User-based collaborative filtering is a method of detecting a user B having similar preferences to those of a certain user A, and extracting an item liked by the user A on the basis of evaluation performed by the user B for a certain item group. For example, when the user B favorably evaluated an item X, the user A is expected to like the item X too. Based on this expectation, the item X can be extracted as information liked by the user A. Incidentally, matrix factorization-based collaborative filtering is a method combining features of user-based collaborative filtering and item-based collaborative filtering. For details of matrix factorization-based collaborative filtering, reference is to be made to Non-Patent Document 1.
In addition, item-based collaborative filtering is a method of detecting an item B having similar features to those of a certain item A, and extracting a user having a liking for the item A on the basis of evaluation performed by a certain user group for the item B. For example, when a user X favorably evaluated the item B, the item A is expected to be liked by the user X too. Based on this expectation, the user X can be extracted as a user having a liking for the item A.
In addition, user-based content-based filtering is for example a method of analyzing, when there is an item group liked by a user A, the preferences of the user A on the basis of the features of the item group, and extracting a new item having features suiting the preferences of the user A. Item-based content-based filtering is for example a method of analyzing, when there is a user group having a liking for an item A, the features of the item A on the basis of the preferences of the user group, and extracting a new user having a liking for the features of the item A.